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Learning algorithms for big data logistic regression on RHIPE platform
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 Title & Authors
Learning algorithms for big data logistic regression on RHIPE platform
Jung, Byung Ho; Lim, Dong Hoon;
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Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.
Big data;Hadoop;logistic regression;R;RHIPE;
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Journal of the Korean Data and Information Science Society, 2017. vol.28. 6, pp.1291-1300 crossref(new window)
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